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02/13/2023
Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler
The development of efficient sampling algorithms catering to non-Euclide...
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11/17/2022
How to Fine-Tune Vision Models with SGD
SGD (with momentum) and AdamW are the two most used optimizers for fine-...
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10/13/2022
Condition-number-independent Convergence Rate of Riemannian Hamiltonian Monte Carlo with Numerical Integrators
We study the convergence rate of discretized Riemannian Hamiltonian Mont...
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07/18/2022
Private Convex Optimization in General Norms
We propose a new framework for differentially private optimization of co...
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03/03/2022
Data Augmentation as Feature Manipulation: a story of desert cows and grass cows
Data augmentation is a cornerstone of the machine learning pipeline, yet...
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02/20/2022
On Optimal Early Stopping: Over-informative versus Under-informative Parametrization
Early stopping is a simple and widely used method to prevent over-traini...
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02/03/2022
Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space
We demonstrate for the first time that ill-conditioned, non-smooth, cons...
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12/23/2021
Analysis of Langevin Monte Carlo from Poincaré to Log-Sobolev
Classically, the continuous-time Langevin diffusion converges exponentia...
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06/10/2021
Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions
We give lower bounds on the performance of two of the most popular sampl...
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02/19/2021
Randomized Exploration is Near-Optimal for Tabular MDP
We study exploration using randomized value functions in Thompson Sampli...
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10/07/2020
Structured Logconcave Sampling with a Restricted Gaussian Oracle
We give algorithms for sampling several structured logconcave families t...
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09/21/2020
Generalized Leverage Score Sampling for Neural Networks
Leverage score sampling is a powerful technique that originates from the...
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06/10/2020
Composite Logconcave Sampling with a Restricted Gaussian Oracle
We consider sampling from composite densities on ℝ^d of the form dπ(x) ∝...
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06/10/2020
When is Particle Filtering Efficient for POMDP Sequential Planning?
Particle filtering is a popular method for inferring latent states in st...
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02/10/2020
Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo
We show that the gradient norm ∇ f(x) for x ∼(-f(x)), where f is strongl...
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09/12/2019
